Update app.py

app.py

@@ -251,60 +251,59 @@ def aggregate_data_yearly(collection):
 #         return 0
 #     return ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100)
 
-def filter_images_by_cloud_percentage(collection, max_cloud_percentage):
-    def calculate_cloud_percentage(image):
-        # Extract the QA60 band
+def calculate_cloud_percentage(image):
+    # Check if QA60 band exists (Level-1C)
+    bands = image.bandNames().getInfo()
+    if 'QA60' in bands:
         qa60 = image.select('QA60')
-        
-        # Identify opaque and cirrus clouds
         opaque_clouds = qa60.bitwiseAnd(1 << 10)
         cirrus_clouds = qa60.bitwiseAnd(1 << 11)
         cloud_mask = opaque_clouds.Or(cirrus_clouds)
-        
-        # Count total pixels and cloudy pixels
-        total_pixels = qa60.reduceRegion(
-            reducer=ee.Reducer.count(),
-            geometry=image.geometry(),
-            scale=60,
-            maxPixels=1e13
-        ).get('QA60')
-        
-        cloudy_pixels = cloud_mask.reduceRegion(
-            reducer=ee.Reducer.sum(),
-            geometry=image.geometry(),
-            scale=60,
-            maxPixels=1e13
-        ).get('QA60')
-        
-        # Calculate cloud percentage
-        cloud_percentage = ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100)
-        
-        # Return the cloud percentage as a property of the image
-        return image.set('cloud_percentage', cloud_percentage)
-
-    # Map the cloud percentage calculation over the collection
-    collection_with_clouds = collection.map(calculate_cloud_percentage)
-    
-    # Filter out images with cloud percentage exceeding the threshold
-    filtered_collection = collection_with_clouds.filter(ee.Filter.lt('cloud_percentage', max_cloud_percentage))
+    elif 'SCL' in bands:  # Check if SCL band exists (Level-2A)
+        scl = image.select('SCL')
+        cloud_mask = scl.eq(8).Or(scl.eq(9)).Or(scl.eq(10))  # Cloud, cirrus, snow/ice
+    else:
+        raise ValueError("Cloud masking bands (QA60 or SCL) not found in the dataset.")
+
+    total_pixels = cloud_mask.reduceRegion(
+        reducer=ee.Reducer.count(),
+        geometry=image.geometry(),
+        scale=60,
+        maxPixels=1e13
+    ).get('constant')  # Use 'constant' for renamed bands
+
+    cloudy_pixels = cloud_mask.reduceRegion(
+        reducer=ee.Reducer.sum(),
+        geometry=image.geometry(),
+        scale=60,
+        maxPixels=1e13
+    ).get('constant')
+
+    if total_pixels == 0:
+        return 0
+    return ee.Number(cloudy_pixels).divide(ee.Number(total_pixels)).multiply(100)
     
-    return filtered_collection
-
-# Preprocessing function
 def preprocess_collection(collection, pixel_cloud_threshold):
     def mask_cloudy_pixels(image):
-        qa60 = image.select('QA60')
-        opaque_clouds = qa60.bitwiseAnd(1 << 10)
-        cirrus_clouds = qa60.bitwiseAnd(1 << 11)
-        cloud_mask = opaque_clouds.Or(cirrus_clouds)
+        bands = image.bandNames().getInfo()
+        if 'QA60' in bands:  # Level-1C
+            qa60 = image.select('QA60')
+            opaque_clouds = qa60.bitwiseAnd(1 << 10)
+            cirrus_clouds = qa60.bitwiseAnd(1 << 11)
+            cloud_mask = opaque_clouds.Or(cirrus_clouds)
+        elif 'SCL' in bands:  # Level-2A
+            scl = image.select('SCL')
+            cloud_mask = scl.eq(8).Or(scl.eq(9)).Or(scl.eq(10))  # Cloud, cirrus, snow/ice
+        else:
+            raise ValueError("Cloud masking bands (QA60 or SCL) not found in the dataset.")
+        
         clear_pixels = cloud_mask.Not()
         return image.updateMask(clear_pixels)
-    
+
     if pixel_cloud_threshold > 0:
         return collection.map(mask_cloudy_pixels)
     return collection
 
-# Process single geometry
 # Process single geometry
 def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None, user_scale=None, pixel_cloud_threshold=0):
     if shape_type.lower() == "point":
@@ -423,10 +422,14 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
 
     st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")
 
-    # Apply cloud filtering at the image level
+    # Apply cloud filtering if applicable
     if pixel_cloud_threshold > 0:
-        raw_collection = filter_images_by_cloud_percentage(raw_collection, pixel_cloud_threshold)
-        st.write(f"Filtered Collection Size (After Cloud Masking): {raw_collection.size().getInfo()}")
+        try:
+            raw_collection = preprocess_collection(raw_collection, pixel_cloud_threshold)
+            st.write(f"Filtered Collection Size (After Cloud Masking): {raw_collection.size().getInfo()}")
+        except Exception as e:
+            st.error(f"Cloud masking failed: {str(e)}")
+            st.stop()
 
     # Use ThreadPoolExecutor to process each geometry
     with ThreadPoolExecutor(max_workers=10) as executor: